Implementation Of Cnn Mobile Netv2 For Classification And Detection Of Diseases In Banana Plants Through Leaf Images

Maria  Yunita; Yustina Yesisanita  Yeyen; Angie Ray  Chanda; Elisabeth Elen  Noweng

doi:10.52436/1.jutif.2026.7.3.5510

Authors

Maria Yunita Informatics engineering, Universitas Nusa Nipa, Indonesia
Yustina Yesisanita Yeyen Physics education, Universitas Nusa Nipa, Indonesia
Angie Ray Chanda Informatics engineering, Universitas Nusa Nipa, Indonesia
Elisabeth Elen Noweng Informatics engineering, Universitas Nusa Nipa, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.3.5510

Keywords:

CNN, Classification, Image, Banana, Plant Disease

Abstract

Banana plants are vulnerable to disease attacks, especially in remote areas with limited access. Banana farmers struggle to identify and classify types of diseases on banana plants early on due to limited information about the types of diseases and the characteristics of diseases that attack bananas.The purpose of this study is he development of a CNN model with a MobileNet architecture for the classification and detection of diseases through banana leaf images, which can be implemented in an Android application. The method used applies a Convolutional Neural Network (CNN) using the MobileNetV2 architecture that can help classify banana plant diseases. The banana leaf image dataset was obtained independently and additionally from the Kaggle platform up to 4135 images. The images were then divided into 6 classes consisting of healthy leaves, panama disease, moko disease, leaf pests, yellow sigatoka and black sigatoka. The image dataset was then divided again into 3 parts: training data, validation data and test data with a data division of 80:10:10. The results showed that CNN with MobileNetV2 architecture can be used for disease classification and detection with an accuracy rate of 87.26% for the test data, 89,59 for validasi and 92.71% for the training data. This model was successfully implemented on the Android platform using Android Studio to detect banana plant diseases in real time without special tools.

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